The Iterative Closest Point (ICP) algorithm is often used to register two sets, or “clouds,” of points with one another. Using ICP, the “source cloud” is transformed to best match the “reference cloud,” such that a measure of the distance between the two clouds is minimized. In its most basic implementation, the ICP algorithm iteratively performs the following sequence of steps, until some condition for convergence is satisfied:
1) For each point in the source cloud, a corresponding point (which may be referred to as the “nearest neighbor”) in the reference cloud is found.
2) The transformation that minimizes the total distance between the two point clouds is computed, the total distance being the sum of the respective distances (computed, for example, as the squared Euclidean distances) between the source points and the corresponding reference points.
3) The source cloud is transformed using the computed transformation.
Rusu, Radu Bogdan, et al., “Aligning point cloud views using persistent feature histograms,” 2008 IEEE/RSJ International Conference on Intelligent Robots and Systems, IEEE, 2008, which is incorporated herein by reference, investigates the usage of persistent point feature histograms for the problem of aligning point cloud data views into a consistent global model. Given a collection of noisy point clouds, the presented algorithm estimates a set of robust 16D features which describe the geometry of each point locally. By analyzing the persistence of the features at different scales, the algorithm extracts an optimal set which best characterizes a given point cloud. The resulted persistent features are used in an initial alignment algorithm to estimate a rigid transformation that approximately registers the input datasets. The algorithm provides good starting points for iterative registration algorithms such as ICP (Iterative Closest Point), by transforming the datasets to its convergence basin.